Continual learning and refinement of causal models through dynamic predicate invention

• URL: http://arxiv.org/abs/2602.17217v1
• Date: Thu, 19 Feb 2026 10:08:31 GMT
• Title: Continual learning and refinement of causal models through dynamic predicate invention
• Authors: Enrique Crespo-Fernandez, Oliver Ray, Telmo de Menezes e Silva Filho, Peter Flach,
• Abstract summary: We propose a framework for constructing symbolic causal world models entirely online.<n>We leverage the power of Meta-Interpretive Learning and predicate invention to find semantically meaningful and reusable abstractions.
• Score: 0.6198237241838559
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficiently navigating complex environments requires agents to internalize the underlying logic of their world, yet standard world modelling methods often struggle with sample inefficiency, lack of transparency, and poor scalability. We propose a framework for constructing symbolic causal world models entirely online by integrating continuous model learning and repair into the agent's decision loop, by leveraging the power of Meta-Interpretive Learning and predicate invention to find semantically meaningful and reusable abstractions, allowing an agent to construct a hierarchy of disentangled, high-quality concepts from its observations. We demonstrate that our lifted inference approach scales to domains with complex relational dynamics, where propositional methods suffer from combinatorial explosion, while achieving sample-efficiency orders of magnitude higher than the established PPO neural-network-based baseline.

Related papers

• From monoliths to modules: Decomposing transducers for efficient world modelling [74.41506965793417]
  We develop a framework for decomposing complex world models represented by transducers.<n>Our results clarify how to invert this process, deriving sub-transducers operating on distinct input-output subspaces.
  arXiv  Detail & Related papers  (2025-12-01T20:37:43Z)
• Object-Centric World Models for Causality-Aware Reinforcement Learning [13.063093054280946]
  We propose emph Transformer Imagination with CAusality-aware reinforcement learning (ASTICA)<n>A unified framework in which object-centric Transformers serve as the world model and causality-aware policy and value networks.<n>Experiments on object-rich benchmarks demonstrate that STICA consistently outperforms state-of-the-art agents in both sample efficiency and final performance.
  arXiv  Detail & Related papers  (2025-11-18T08:53:09Z)
• Step-Aware Policy Optimization for Reasoning in Diffusion Large Language Models [57.42778606399764]
  Diffusion language models (dLLMs) offer a promising, non-autoregressive paradigm for text generation.<n>Current reinforcement learning approaches often rely on sparse, outcome-based rewards.<n>We argue that this stems from a fundamental mismatch with the natural structure of reasoning.
  arXiv  Detail & Related papers  (2025-10-02T00:34:15Z)
• Unveiling the Actual Performance of Neural-based Models for Equation Discovery on Graph Dynamical Systems [45.11208589443806]
  Kolmogorov-Arnold Networks (KANs) for graphs are designed to exploit their inherent interpretability.<n>KANs successfully identify the underlying symbolic equations, significantly surpassing existing baselines.<n>This study offers a practical guide for researchers, clarifying the trade-offs between model expressivity and interpretability.
  arXiv  Detail & Related papers  (2025-08-25T16:25:50Z)
• World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks [55.90051810762702]
  We present a comprehensive overview of world models, highlighting their architecture, training paradigms, and applications across prediction, generation, planning, and causal reasoning.<n>We propose Wireless Dreamer, a novel world model-based reinforcement learning framework tailored for wireless edge intelligence optimization.
  arXiv  Detail & Related papers  (2025-05-31T06:43:00Z)
• Consistent World Models via Foresight Diffusion [56.45012929930605]
  We argue that a key bottleneck in learning consistent diffusion-based world models lies in the suboptimal predictive ability.<n>We propose Foresight Diffusion (ForeDiff), a diffusion-based world modeling framework that enhances consistency by decoupling condition understanding from target denoising.
  arXiv  Detail & Related papers  (2025-05-22T10:01:59Z)
• Verbalized Probabilistic Graphical Modeling [8.524824578426962]
  We propose Verbalized Probabilistic Graphical Modeling (vPGM) to simulate key principles of Probabilistic Graphical Models (PGMs) in natural language.<n> vPGM bypasses expert-driven model design, making it well-suited for scenarios with limited assumptions or scarce data.<n>Our results indicate that the model effectively enhances confidence calibration and text generation quality.
  arXiv  Detail & Related papers  (2024-06-08T16:35:31Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• VDFD: Multi-Agent Value Decomposition Framework with Disentangled World Model [10.36125908359289]
  We propose a novel model-based multi-agent reinforcement learning approach named Value Decomposition Framework with Disentangled World Model.<n>Our method achieves high sample efficiency and exhibits superior performance compared to other baselines across a wide range of multi-agent learning tasks.
  arXiv  Detail & Related papers  (2023-09-08T22:12:43Z)
• Latent Variable Representation for Reinforcement Learning [131.03944557979725]
  It remains unclear theoretically and empirically how latent variable models may facilitate learning, planning, and exploration to improve the sample efficiency of model-based reinforcement learning. We provide a representation view of the latent variable models for state-action value functions, which allows both tractable variational learning algorithm and effective implementation of the optimism/pessimism principle. In particular, we propose a computationally efficient planning algorithm with UCB exploration by incorporating kernel embeddings of latent variable models.
  arXiv  Detail & Related papers  (2022-12-17T00:26:31Z)
• Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders [22.54887526392739]
  We propose a novel approach to training models with deep-latent hierarchies based on Optimal Transport. We show that our method enables the generative model to fully leverage its deep-latent hierarchy, avoiding the well known "latent variable collapse" issue of VAEs.
  arXiv  Detail & Related papers  (2020-10-07T15:04:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.